JPWO2019146200A1 - Information processing equipment, information processing methods, and recording media - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing device, an information processing method, and a recording medium capable of automatically generating a code of conduct of a community and promoting spontaneous behavior change. SOLUTION: Sensor data obtained by sensing a member belonging to a specific community is acquired, a code of conduct in the specific community is automatically generated based on the acquired sensor data, and the code of conduct is based on the code of conduct. An information processing device equipped with a control unit that controls and encourages members to change their behavior. [Selection diagram] Fig. 1

Description

The present disclosure relates to information processing devices, information processing methods, and recording media.

In recent years, agents have been provided that recommend contents and actions according to user's questions, requests, and contexts using dedicated terminals such as smartphones, tablet terminals, and home agents. Such agents were intended to improve the current short-term convenience and comfort of users. For example, an agent who answers a question, answers the weather, sets an alarm clock, and manages a schedule closes the response to a question or task in one short-term session (complete with request and response). ing.

On the other hand, there are the following prior arts that promote behavior change to gradually approach problem solving from a long-term perspective.

For example, in Patent Document 1 below, in fields such as health management, education, rehabilitation, and autism treatment, it is determined from the target and the behavior data of the subject, which of the behavior change stages the subject corresponds to, and based on the determination. Therefore, a behavior support system equipped with a means for selecting an intervention method that changes the behavior of the subject is disclosed.

Further, Patent Document 2 below discloses a support device for automatically determining a behavior change stage by an evaluation unit having evaluation conditions for evaluation rules, which is automatically generated using learning data. Specifically, the behavior change stage can be determined from the conversation between the instructor of the metabolic insurance guidance and the target person.

Japanese Unexamined Patent Publication No. 2016-85703 Japanese Unexamined Patent Publication No. 2010-102643

However, in each of the above-mentioned prior arts, a specific problem is predetermined, and the problem itself has not been determined. In addition, the stage of behavior change was predetermined, and it was only possible to judge a specific event based on the rules.

Moreover, in a specific community such as a family, the smaller the group, the more likely it is that the code of conduct will differ between the communities, but the code of conduct has not been generated for each specific community.

Therefore, this disclosure proposes an information processing device, an information processing method, and a recording medium capable of automatically generating a code of conduct for a community and promoting spontaneous behavior change.

According to the present disclosure, sensor data obtained by sensing a member belonging to a specific community is acquired, a code of conduct in the specific community is automatically generated based on the acquired sensor data, and the code of conduct is based on the code of conduct. , Propose an information processing device provided with a control unit that controls the members to encourage them to change their behavior.

According to the present disclosure, by sensing the member belonging to the specific community according to the code of conduct in the specific community automatically generated in advance based on the sensor data obtained by sensing the member belonging to the specific community. We propose an information processing device provided with a control unit that controls and encourages the members to change their behavior according to the obtained sensor data.

According to the present disclosure, a processor acquires sensor data obtained by sensing a member belonging to a specific community, and automatically generates a code of conduct in the specific community based on the acquired sensor data. And, based on the code of conduct, we propose an information processing method including encouraging the members to change their behavior.

According to the present disclosure, a computer acquires sensor data obtained by sensing a member belonging to a specific community, automatically generates a code of conduct in the specific community based on the acquired sensor data, and performs the action. Based on the norms, we propose a recording medium in which a program for functioning as a control unit that controls the members to change their behavior is recorded.

As described above, according to the present disclosure, it is possible to automatically generate a code of conduct for the community and promote voluntary behavior change.

It should be noted that the above effects are not necessarily limited, and together with or in place of the above effects, any of the effects shown herein, or any other effect that can be grasped from this specification. May be played.

It is a figure explaining the outline of the information processing system by one Embodiment of this disclosure. It is a block diagram which shows an example of the structure of the information processing system by one Embodiment of this disclosure. It is a flowchart which shows the operation process of the information processing system by one Embodiment of this disclosure. It is a figure explaining the outline of the master system of 1st Example by this Embodiment. It is a block diagram which shows the structural example of the master system of 1st Example by this Embodiment. It is a figure which shows an example of the problem index of 1st Example by this Embodiment. It is a figure explaining the causal analysis of the 1st Example by this Embodiment. It is a figure explaining the search of the causal path based on the causal analysis result of the 1st Example by this Embodiment. It is a table which shows the probability distribution of the breakfast start time and the grouping time (hour / week) in the 1st Example by this Embodiment. It is a table which shows the probability distribution of the wake-up time and the breakfast start time in the 1st Example by this Embodiment. It is a figure explaining the matrix operation which obtains the probability distribution between the wake-up time and the grouping time in the 1st Example by this Embodiment. It is a figure which shows the table of the probability distribution between the wake-up time and the grouping time obtained as a result of the matrix operation shown in FIG. It is a flowchart which shows the whole flow of the operation process of 1st Example by this Embodiment. It is a flowchart which shows the problem estimation process of 1st Example by this Embodiment. It is a flowchart which shows the intervention reservation process of 1st Example by this Embodiment. It is a flowchart which shows the intervention process of 1st Example by this Embodiment. It is a figure which shows an example of the causal path of the difference in the sense of values of the 1st Example by this Embodiment. It is a block diagram which shows an example of the structure of the master system of 2nd Example by this Embodiment. It is a basic flowchart of the operation process of 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about the food discipline of the 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about the dish cleanup of the 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about the cleanup of the office desk of the 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about the room tidying up of the 2nd Example by this Embodiment. It is a figure explaining an example of the information presentation which promotes the behavior change in the example shown in FIG. It is a flowchart which shows the behavior change processing about the baby crying of the 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about a toy of 2nd Example by this Embodiment. It is a flowchart which shows the behavior change processing about the total value of the 2nd Example by this Embodiment. It is a table which shows the calculation example of each candidate of the total value of the 2nd Example by this Embodiment. It is a block diagram which shows an example of the structure of the master system of 3rd Example by this Embodiment. It is a graph which shows an example of the meal record of the 3rd Example by this Embodiment. It is a figure explaining the separation of the life rhythm of the 3rd Example by this embodiment. It is a flowchart which shows the operation process which generates the rhythm of the dinner time of 3rd Example by this Embodiment. It is a figure which shows an example of the calculation formula of the cumulative average time for each day of the week of the 3rd Example by this Embodiment. It is a flowchart which generates advice based on the life rhythm of 3rd Example by this Embodiment. It is a flowchart which promotes the adjustment (behavior change) of a life rhythm according to the overlap of the event by the modification of the 3rd Example by this Embodiment. It is a block diagram which shows an example of the hardware composition of the information processing apparatus which concerns on this embodiment.

Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

In addition, the explanation shall be given in the following order.
1. 1. Outline of the information processing system according to the embodiment of the present disclosure 2. First Example (problem estimation and behavior change)
2-1. Configuration example 2-2. Operation processing 2-3. Supplement 3. Second Example (Generation of Value Standards and Behavior Modification)
3-1. Configuration example 3-2. Operation processing 4. Third Example (Adjustment of Life Rhythm)
4-1. Configuration example 4-2. Operation processing 4-3. Modification example 5. Hardware configuration example 6. Summary

<< 1. Outline of information processing system according to one embodiment of the present disclosure >>
FIG. 1 is a diagram illustrating an outline of an information processing system according to an embodiment of the present disclosure. As shown in FIG. 1, in the information processing system according to the present embodiment, a virtual agent (a master existence of a specific community. In the present specification, hereinafter, the following, in accordance with a predetermined code of conduct, for each community 2A to 2C such as a family member. There are master systems 10A to 10C that promote behavioral change by (referred to as "master"). In FIG. 1, the master systems 10A to 10C are anthropomorphized and illustrated. The master systems 10A to 10C can solve community problems by automatically generating a code of conduct based on the behavior record of each user in a specific community and indirectly promoting behavior change based on the code of conduct. .. On the user side, while acting according to the words of the master, problems in the community can be solved or the value standards can be adjusted without being aware of the code of conduct (issues and value standards). It will be possible to improve the situation in the community.

(background)
As described above, in each of the conventional master systems, a specific problem is predetermined, and the problem itself is not determined. In addition, the conventional master system is closed in one short session that is completed by request and response, but in real life, there are many problems in which multiple factors are intricately intertwined, and such problems are direct. Or it cannot be solved in the short term.

In addition, the content and solution of the problem are not the same. For example, in the case of a family problem, the seriousness of the problem and the solution may differ depending on the code of conduct and the environment of the process. To that end, it is important to analyze the relationships between multiple factors to find long-term, non-short-term solutions, and where to intervene. Even if one code of conduct is taken, in a specific community such as a family, the smaller the group, the more likely it is that the code of conduct will differ between communities, and that community's own "ethics" exists. The possibility of doing so also increases. Therefore, it will be impossible to limit the code of conduct to one general-purpose item or to standardize the collection of all big data, so collect data by paying attention to a specific community such as a family. However, it is important to clarify the code of conduct in a specific community.

Therefore, in the present embodiment, as shown in FIG. 1, a master system 10 capable of automatically generating a code of conduct for each specific community and promoting spontaneous behavior change is provided.

FIG. 2 is a block diagram showing an example of the configuration of the information processing system (master system 10) according to the embodiment of the present disclosure. As shown in FIG. 2, the master system 10 according to the present embodiment includes a data analysis unit 11, a code of conduct generation unit 12, and a behavior change instruction unit 13. The master system 10 may be configured by a server on the network, or may be configured by a dedicated terminal such as a home agent or a client device such as a smartphone or tablet terminal.

The data analysis unit 11 analyzes the sensing data that senses the behavior of a user belonging to a specific community such as a family.

The code of conduct generation unit 12 generates a code of conduct for a specific community based on the analysis result by the data analysis unit 11. Here, the "code of conduct" is a means for solving the problems of the specific community (for example, estimating the problem of short grouping time and automatically generating a code of conduct to increase the grouping time), or its. The generation (estimation) of value standards in a specific community can be mentioned.

The behavior change instruction unit 13 controls notifications and the like for urging users in a specific community to change their behavior according to the code of conduct generated by the code of conduct generation unit 12.

The operation processing of the master system 10 having such a configuration is shown in FIG. As shown in FIG. 3, first, the master system 10 collects the sensor data of the specific community (step S103), and the data analysis unit 11 analyzes the sensor data (step S106).

Next, the code of conduct generation unit 12 generates a code of conduct for a specific community based on the analysis result by the data analysis unit 11 (step S109), and if it can be generated (step S109 / Yes), information on the code of conduct. (Step S112).

Next, the behavior change instruction unit 13 determines an event to be intervened that can change the behavior based on the code of conduct (step S115 / Yes). For example, judgment of an event (wake-up time, exercise frequency, etc., or life rhythm) that can change behavior in order to realize a code of conduct, or a situation that deviates from the value standard is judged as an event to be intervened.

Then, when an event to be intervened that can change the behavior is found (step S115 / Yes), the behavior change instruction unit 13 indirectly urges the user in the specific community to change the behavior (step S118). .. Specifically, the behavior change instruction unit 13 indirectly encourages behavioral changes, adjustment of life rhythms, or actions to eliminate deviations from value standards. By automatically generating a code of conduct in this way and indirectly promoting behavioral change, each user in a specific community acts according to the master without being aware of the code of conduct (issues and value standards). Before you know it, you will be able to solve problems within a specific community and take actions according to value standards, and you will be able to improve the situation in a specific community.

The outline of the information processing system according to the embodiment of the present disclosure has been described above. The configuration of this embodiment is not limited to the example shown in FIG. For example, if you already have a code of conduct in a specific community that is automatically generated in advance based on sensor data obtained by sensing members belonging to a specific community, it was obtained by sensing members belonging to the specific community. It may be an information processing device that controls according to the sensor data (data analysis unit 11) to encourage the members to change their behavior (behavior change instruction unit 13). Subsequently, the information processing system according to the present embodiment will be specifically described with reference to the first to third embodiments. In the first embodiment, "analysis of means for solving an estimated problem" is performed as a generation of a code of conduct, and behavior change is indirectly promoted in order to solve the problem. Further, in the second embodiment, it will be described that "generation of a value standard" is performed as the generation of a code of conduct, and if it deviates from the value standard, behavior change is indirectly promoted. Further, in the third embodiment, it will be described that the life rhythm is adjusted as a behavior change for solving the problem estimated by the first embodiment.

<< 2. First Example (problem estimation and behavior change) >>
First, the master system 10-1 according to the first embodiment will be described with reference to FIGS. 4 to 17. In this example, by performing daily data collection and daily analysis in a small community such as a family unit (Casual Analysis), problems occurring in the family can be found, and the problems can be seen from a long-term perspective. Perform interventions that encourage behavior change to resolve. That is, the family problem is estimated based on the family data collected on a daily basis, and the objective variable is automatically generated as a code of conduct for solving the problem (for example, "grouping time (increase)"), and the purpose. Create a relationship graph of factor variables starting from variables, and based on the relationship graph, intervention points that promote behavioral change to set the objective variable to the desired value (for example, "late night drinking", "exercise intensity" Find and intervene in factor variables) and guide members to resolve family issues over a long period of time (eg, behavioral transformations for members so that factor variables associated with the objective variable approach desired values. To encourage). For example, by using CALC (registered trademark, Calc), which is a causal analysis algorithm provided by Sony Computer Science Laboratory Co., Ltd., it is possible to analyze complex causal relationships between multiple variables. is there.

FIG. 4 is a diagram illustrating an outline of the first embodiment. The first embodiment is roughly performed according to the flow shown in FIG. That is, A: daily behavior monitoring, B: task estimation, C: task is automatically set as the objective variable of causal analysis, and D: intervention is performed at the point and timing at which intervention is possible (promotes behavior change). .. By repeating the processes shown in A to D on a daily basis, behavior change occurs, and problems within a specific community are gradually solved.

(A: Daily behavior monitoring)
In this embodiment, the more types of information that can be sensed, the wider the range of data that can be used for analysis. The data to be analyzed is not limited to a specific type of data. For example, in the prior art, the data to be used for analysis may be determined in advance for a limited purpose, but this is not necessary in this embodiment, and the types of data that can be acquired are increased at any time (registered in the database at any time). It is possible to go).

(B: Problem estimation)
In this embodiment, for example, an index list that describes the relationship between an index that can be a problem related to a family, which is an example of a specific community, and an index of a sensor or behavior history necessary for calculating the index (for example, "index list related to family relationship") ) Can be used to estimate the problem. Specifically, the value of each sensor / behavior history index is confirmed, and it is determined whether or not the index that can be a problem (for example, "family gathering time") is below (or exceeds) the threshold value. .. This process is performed for the number of items in the list, and it is possible to list the issues that the family has.

(C: Tasks are automatically set as objective variables for causal analysis)
Causal analysis is performed using the detected problem as the objective variable and other sensor / behavior history information as the explanatory variable. At this time, not only the index related to the problem of the index list but also other indexes may be input as the explanatory variable. If there are multiple tasks, each is used as an objective variable and analyzed multiple times individually.

(D: Master intervenes at points and timings where intervention is possible)
The result of the analysis is a graph structure in which factors directly related to the objective variable are connected and another factor is attached to the tip of that factor. By tracing this graph from the objective variable as the starting point, it becomes possible to investigate the causal relationship retroactively from the result to the cause direction. At this time, each factor is flagged as to whether or not it is possible to intervene (for example, the wake-up time), and if intervention is possible, the master intervenes in the factor to promote behavior change in the direction of improving the result. In addition to the method of directly instructing the user, indirect intervention such as playing relaxing music and optimizing the wake-up time is also possible as a method of promoting behavior change.

<2-1. Configuration example>
FIG. 5 is a block diagram showing a configuration example of the master system 10-1 of the first embodiment. As shown in FIG. 5, the master system 10-1 includes an information processing device 20, an environment-side sensor 30, a user-side sensor 32, a service server 34, and an output device 36.

(Sensor group)
The environment side sensor 30, the user side sensor 32, and the service server 34 are examples of acquisition destinations of information about users (members) belonging to a specific community, and the present embodiment is not limited to these, and all of them are used. It is not limited to the including configuration.

The environment side sensor 30 is provided on the environment side, for example, a camera installed in a room, a microphone (hereinafter referred to as a microphone), a distance sensor, an illuminance sensor, a pressure / vibration sensor installed on a table, a chair, a bed, or the like. Includes various sensors. By detecting the smile amount in the community unit by the environment side sensor 30, for example, by deciding to take the smile amount with the fixed camera in the living room, it is possible to obtain the smile amount under the same conditions in the home.

The user-side sensor 32 includes an acceleration sensor, a gyro sensor, a geomagnetic sensor, a position sensor, a heartbeat, a body temperature, etc. provided in a smartphone, a mobile phone terminal, a tablet terminal, a wearable device (HMD, smart eyeglass, smart band, etc.). Includes various sensors such as biosensors, cameras and microphones.

The service server 34 is assumed to be an SNS server, a location information acquisition server, and an electronic commerce server (e-commerce site, etc.) used by users belonging to a specific community, and information about the user from other than the sensor (movement history, shopping history, etc.) Information on user behavior, etc.) can be obtained from the network.

(Information processing device 20)
The information processing device 20 (causal analysis server) includes a receiving unit 201, a transmitting unit 203, an image processing unit 210, a voice processing unit 212, a sensor / behavior data processing unit 214, a factor variable DB (database) 220, and an intervention device DB 224. It has an intervention rule DB 226, an intervention reservation DB 228, a task index DB 222, a task estimation unit 230, a causal analysis unit 232, and an intervention unit 235. The image processing unit 210, the voice processing unit 212, the sensor / behavior data processing unit 214, the task estimation unit 230, the causal analysis unit 232, and the intervention unit 235 can be controlled by a control unit provided in the information processing device 20. The control unit functions as an arithmetic processing unit and a control device, and controls the overall operation in the information processing device 20 according to various programs. The control unit is realized by an electronic circuit such as a CPU (Central Processing Unit) or a microprocessor. Further, the control unit may include a ROM (Read Only Memory) for storing programs to be used, calculation parameters, and the like, and a RAM (Random Access Memory) for temporarily storing parameters and the like that change as appropriate.

The information processing device 20 may be configured by a cloud server on the network, an intermediate server or an edge server, or a dedicated terminal placed in a home such as a home agent. It may be composed of an information processing terminal such as a PC or a smartphone.

-Receiving unit 201 and transmitting unit 203
The receiving unit 201 acquires sensor information and behavior data of each user belonging to a specific community from the environment side sensor 30, the user side sensor 32, and the service server 34. Further, the transmission unit 203 transmits a control signal instructing output control for indirectly promoting behavior change to the output device 36 according to the processing by the intervention unit 235.

The receiving unit 201 and the transmitting unit 203 are composed of a communication unit (not shown) provided in the information processing device 20. The communication unit connects to external devices such as the environment side sensor 30, the user side sensor 32, the service server 34, and the output device 36 by wire or wirelessly, and transmits / receives data. The communication unit is, for example, a wired / wireless LAN (Local Area Network), Wi-Fi (registered trademark), Bluetooth (registered trademark), mobile communication network (LTE (Long Term Evolution)), 3G (third generation mobile communication). Communication connection with an external device by method)).

-Data processing unit Various sensor information and behavior data of users belonging to a specific community are appropriately processed by the image processing unit 210, the voice processing unit 212, and the sensor / behavior data processing unit 214. Specifically, the image processing unit 210 performs person recognition, facial expression recognition, object recognition, and the like from the image of the camera. In addition, the voice processing unit 212 performs conversation recognition, speaker recognition, conversation positive / negative recognition, emotion recognition, and the like from the voice of the microphone. Further, depending on the sensor, the sensor / behavior data processing unit 214 does not record the raw data as it is, but performs processing such as converting it into a meaningful label (for example, sitting down from the vibration sensor of the chair). Converted to time spent). In addition, the sensor / behavior data processing unit 214 extracts the user's behavior context (such as a family meal at a restaurant) indicating where and what they are doing from SNS information and location information (GPS, etc.). In addition, the sensor / behavior data processing unit 214 extracts positive / negative emotions from the sentences posted on the SNS, who was with the interaction information between users, who sympathized with them, and so on. It is also possible to extract information.

The data subjected to the above processing is stored in the factor variable DB 220 as a variable for task estimation and causal analysis. Hereinafter, the variable stored in the factor variable DB 220 will be referred to as a "factor variable". For example, the types of factor variables shown in Table 1 below can be considered, but the types of factor variables according to this embodiment are not limited to this, and any index that can be obtained may be used.

・ Problem estimation unit 230
The task estimation unit 230 investigates the values of the factor variables associated with each task index registered in the task index DB 222, and determines whether or not a task has occurred (estimates the task). FIG. 6 shows an example of the problem index according to this embodiment. As shown in FIG. 6, for example, "conversation time with a daughter", "family gathering time", "children's rebellion time", "marital quarrel time", etc. are preset as task indexes related to family relations. There is. Examples of the factor variables of each task index include items as shown in FIG. In addition, the task index DB 222 is also associated with a condition for determining that there is a task based on the factor variable. For example, the task of "family gathering time" is judged by the time when the family is at the table together, the time of positive conversation, the amount of smile, etc., but more specifically, "the conversation time of one week" Conditions such as "3 hours or less", "Weekday breakfast with all members on average 2 days or less per week", and "Positive conversation rate is 30% or less of conversation content" can be mentioned.

The task estimation unit 230 may determine that there is a problem when all the conditions presented in the task index are satisfied, or may consider that there is a problem if any one of them is applicable. Good. In addition, it is possible to set in advance whether to consider that there is a problem when all the conditions are met, or whether to consider that there is a problem when any one condition is met, so that complicated conditions can be set for each problem. You can add a flag. The factor variables used here are factor variables that are described in advance on a rule base that is associated with each task index in advance by a person.

・ Causal analysis department 232
When the problem is estimated by the task estimation unit 230 (it is determined that the problem is occurring), the causal analysis unit 232 performs the causal analysis of the problem. Traditionally, the estimation of statistical causal relationships from observational data in multivariate random variables has been performed by maximizing the estimation result by the information criterion, maximum likelihood method with penalties, or Bayesian method as a score, and conditional between variables. It is roughly divided into the method of estimating by the statistical test of independence. Expressing the causal relationship between the resulting variables as a graphical model (non-circular model) is often done because of the readability of the results. The algorithm for causal analysis is not particularly limited, but for example, CALC provided by Sony Computer Science Laboratories, Inc. described above may be used. CALC is a technology that has already been commercialized as a causal analysis technology for large-scale data (https://www.isid.co.jp/news/release/2017/0530.html, https: // www). .isid.co.jp/solution/calc.html).

Specifically, the causal analysis unit 232 sets the task as the objective variable, and all or part of the other factor variables (basically, it is better to include all of them, but the number of data is limited due to the limitation of calculation time and memory. You may narrow down by preferentially entering the factor variable order with the most data or the factor variable for which a lot of data has been obtained recently) as the explanatory variable, and perform causal analysis. FIG. 7 is a diagram illustrating a causal analysis according to this embodiment.

As shown in FIG. 7, for example, when a problem of "grouping time" is estimated, this is set as an objective variable. Regarding the objective variable, if the variable stored in the factor variable DB 220 cannot be applied as it is, it may be dynamically generated. For example, "group time" cannot be sensed directly, so it is generated by combining other variables. Specifically, by combining variables such as the time when all the family members are at the table at the same time, the time when positive conversation is taking place, and the time when the smile level is above a certain level, the total time of a fun group or the group The quality of the table is derived and used as the objective variable. The information processing apparatus 20 may have the variable combination rule as a knowledge base in advance, or it may be automatically extracted.

The causal analysis unit 232 outputs the analysis result to the intervention unit 235.

・ Intervention Department 235
The intervention unit 235 examines the causal analysis result, traces the arrow in the opposite direction from the factor variable directly connected to the objective variable, and extracts a plurality of causal paths by tracing back until there is no arrow that can be traced. Depending on the analysis method used, arrows are not always attached, and there are cases where they are connected by a simple straight line. In such a case, the direction of the arrow is conveniently determined by utilizing the characteristics of the causal analysis method to be used. For example, it is conceivable to treat the factor variable far from the objective variable (how many factor variables are sandwiched between them) as having a convenient arrow toward the factor variable close to the factor variable. Even when factor variables with the same distance from the objective variable are connected by a straight line, the convenient orientation is determined in consideration of the characteristics of the method used in the same way.

Here, FIG. 8 shows a diagram illustrating a search for a causal path based on the causal analysis result of this example. For example, the intervention unit 235 traces the causal path (arrow 2105) connected to the “group time” (objective variable 2001) in the opposite direction (arrow 2200) starting from the “group time” shown in FIG. The causal path (arrow 2104) connected to the factor variable 2002 is also traced in the opposite direction (arrow 2201), and the search for such a causal path is continued until it cannot be traced. That is, the arrows 2105 to 2100 of the causal path shown in FIG. 8 are sequentially followed in the opposite directions (arrows 2200 to 2205). The search for the path ends when the arrow cannot be traced in the opposite direction (when the factor variable 2007 with the mark 2300 shown in FIG. 8 is reached). In FIG. 8, as an example of a causal path composed of factor variables, “exercise intensity → stress amount → drinking amount after 22:00 → sleep quality → wake-up time → family breakfast start time → grouping time” can be extracted. In the search for the causal path, the intervention unit 235 traces the arrow of the causal path in the opposite direction, starting from the "grouping time", and examines the relationship between a certain factor variable and the factor variable one upstream.

For example, the intervention unit 235 looks at the "group time", "family breakfast start time", and "wake-up time (of myself = father)" in relation to the probability distribution, and sets the value of the objective variable within the target range. Calculate the value of the upstream factor variable to obtain the highest expected value. The calculation of the expected value by the calculation of the probability distribution between the factor variables will be described with reference to FIGS. 9 to 14. FIG. 9 is a table showing the probability distribution of breakfast start time and grouping time (hours / week). According to the table shown in FIG. 9, it can be seen that the expected value of the gathering time is highest when breakfast is started from 7:30 to 8:00. Specifically, the grouping time of the objective variable (since it has a range, the median value such as 0.5,1.5,2.5,3.5,5.0,6.0 is used as the representative value) and the respective probabilities (0.000,0.029,0.124). , 0.141, 0.284, 0.442) are multiplied in order, and the sum is the expected value of the group. In the case of this example, 4.92 hours is the expected value. When the expected value was calculated for other time zones in the same way, as a result, the most gathering time was when breakfast was started from 7:30 to 8:00.

FIG. 10 is a table showing the probability distribution of the wake-up time and the breakfast start time. According to the table shown in FIG. 10, it can be seen that the breakfast start time is most likely to be from 7:30 to 8:00 when waking up from 7:00 to 7:30. The probability distribution between two connected adjacent factor variables can be obtained by cross-tabulating the original data.

FIG. 11 is a diagram illustrating a matrix operation for obtaining a probability distribution between the wake-up time and the grouping time. When the table shown in FIG. 9 is A and the table shown in FIG. 10 is B, it is possible to obtain the probability distribution between the wake-up time and the gathering time by the matrix operation shown in FIG.

FIG. 12 is a diagram showing a table of probability distributions between the wake-up time and the gathering time obtained as a result of the matrix operation shown in FIG. As shown in FIG. 12, when waking up from 7:00 to 7:30, there is a 24.3% probability that the grouping time will be 6 hours or more, and a 70% probability that the grouping time will be 3 hours or more. On the other hand, when the wake-up time is after 8:30, there is a probability of about 81% that the gathering time will be less than 2 hours.

In this way, by repeatedly multiplying the conditional probability table upstream, it is possible that the value of the objective variable will be the target value when each factor variable of the causal path finally takes a value. Understand.

As an analysis method other than CALC, for example, an analysis method called a Bayesian network that stochastically expresses the relationship between each variable can be used. When this method is applied, the variables (nodes) and the arrows (or line segments) that connect them do not represent a causal relationship, but it is convenient because the connected variables are related to each other. It is possible to apply this example as a causal factor. In this embodiment, even if the term "causality" is not used, it is possible to apply the relationship between variables as a cause and effect for convenience.

Next, the intervention unit 235 searches for a factor variable with an intervention possible flag from the causal path, and acquires an intervention method for the factor variable from the intervention rule DB 226. When storing factor variables in the database, they are flagged as intervening. This flag may be added by a person in advance, or the raw sensor data may be flagged as intervening in advance, and even one sensor data with the intervening flag when the factor variable is generated is included. If so, a method may be taken in which factor variables can also be intervened. In the example shown in FIG. 8, for example, the factor variable 2003 (wake-up time), the factor variable 2005 (drinking amount after 22:00), and the factor variable 2007 (exercise intensity) are flagged as intervening. For example, the wake-up time is registered in the database with the intervention possible flag attached because the intervention is possible by setting the alarm clock of the wearable device. As described above, in this embodiment, the points that can be intervened are extracted from the factor variables that trace the causal path in the opposite direction from the objective variable, and indirect behavior change (intervention) can be performed. Here, an example of the intervention rule registered in the intervention rule DB226 is shown in Table 2 below.

In reality, the factor variables and intervention methods are not infinite, and the intervention rules settle in a certain pattern, so they may be shared by all agents on the cloud side. However, the parameters (wake-up time and target exercise intensity) set at the time of intervention may differ depending on the individual and family.

After acquiring the intervention method, the intervention unit 235 searches the intervention device DB 224 for a device having a target device capability. A device that can be used by a user or a family member is registered in the intervention device DB 224. Here, an example of the intervention device is shown in Table 3 below.

When an appropriate intervention device is found, the intervention unit 235 registers the device ID, activation condition, intervention command, and parameter in the intervention reservation DB 228. An example of intervention reservation is shown in Table 4 below.

Next, the intervention unit 235 transmits a command and a parameter from the transmission unit 203 to the designated device when the conditions are met, based on the reservation data registered in the intervention reservation DB 228.

(Output device 36)
The output device 36 is a device that encourages users belonging to a specific community to indirectly change their behavior to solve a problem under the control of the intervention unit 235. The output device 36 may include a wide range of IoT devices such as smartphones, tablet terminals, mobile phone terminals, PCs, wearable devices, TVs, lighting devices, speakers, or table clocks.

The output device 36 that receives the command from the information processing device 20 performs an intervention operation according to the expression method of the device. For example, when the information processing device 20 transmits a sound and vibration command to the smartphone together with a set time, the corresponding application on the smartphone side sets an alarm at that time. Alternatively, when the information processing device 20 throws a command to the corresponding smart speaker, the speaker plays music at a designated time. Further, when the information processing device 20 transmits the coupon to the smartphone, it is displayed by a push notification, and the coupon is further displayed on the browser. Further, when the information processing device 20 transmits to a PC or the like, the PC may automatically replace it with an e-mail and notify the user. By converting and outputting to a display method suitable for each output device 36 in this way, it is possible to use any device without depending on a specific model or device.

<2-2. Operation processing>
Subsequently, the processing according to each configuration described above will be described with reference to the flowchart.

(2-2-1. Overall flow)
FIG. 13 is a flowchart showing the entire flow of the operation processing of the first embodiment. As shown in FIG. 13, first, the information processing device 20 inputs data from the sensor group (step S103).

Next, depending on the type of sensor, the image processing unit 210, the voice processing unit 212, and the sensor / behavior data processing unit 214 generate factor variables (step S106).

Next, it is determined whether or not there is an intervention reservation that satisfies the intervention condition (step S109), if there is no intervention reservation, a task estimation process is performed (step S112), and if there is, an intervention action process (step S133) is performed. .. Note that step S109 is not limited to being performed at this timing, and may be processed in parallel with the process shown in FIG. The flow of the task estimation process is shown in FIG. The flow of the intervention operation process is shown in FIG.

Next, when a task is estimated (step S115 / Yes), the causal analysis unit 232 sets the task as an objective variable (step S118) and performs causal analysis (step S121).

Next, the intervention unit 235 extracts points (causal variables, events) that can intervene (behavior changeable) from the causal variables included in the causal path (step S124).

Then, when a point where intervention is possible is found (step S127 / Yes), the intervention unit 235 determines the intervention action and adds an intervention reservation (step S130). The process from the extraction of the intervention point to the addition of the intervention reservation will be described in more detail with reference to FIG.

(2-2-2. Task estimation process)
FIG. 14 is a flowchart showing the task estimation process. As shown in FIG. 14, first, the task estimation unit 230 emptys the task list (step S143) and acquires the task index (see FIG. 6) from the task index DB 222 (step S146).

Next, the task estimation unit 230 selects one unprocessed task from the acquired task index (step S149).

Next, the task estimation unit 230 selects one unprocessed factor variable from the task factor variable list (see FIG. 6) (step S152), and checks the value of the selected factor variable (step S155). That is, the task estimation unit 230 determines whether or not the value of the selected factor variable satisfies the condition for determining that there is a task associated with the task index (see FIG. 6).

Next, when it is determined that there is a problem (step S158 / Yes), the task estimation unit 230 adds the task to the task list (step S161).

Next, the task estimation unit 230 repeats the processes of steps S152 to S161 until the values of all the factor variables listed in the factor variable list of the task are checked (step S164).

Further, when all the tasks listed in the task index have been checked (step S167 / No), the task estimation unit 230 displays the task list (causal analysis unit) when there is a problem in the task list (step S170 / Yes). (To 232) (step S176). On the other hand, when there is no problem in the task list (step S170 / No), the process returns to the status of no task (returning to the process of step S115 shown in FIG. 13) (step S179).

(2-2-3. Intervention reservation processing)
FIG. 15 is a flowchart showing the intervention reservation process. As shown in FIG. 15, first, the intervention unit 235 sets the objective variable (problem) of the analysis result by the causal analysis unit 232 as the starting point of the causal path generation (step S183).

Next, the intervention unit 235 traces the directions of all the arrows in the opposite direction from the starting point and repeats until the factor variable at the end is reached to generate all the causal paths (step S186).

The intervention unit 235 then generates a probability distribution table between the two connected factor variables on the causal path (step S189).

Next, the intervention unit 235 performs matrix multiplication while tracing the probability distribution table upstream of the causal path to obtain the probability distribution between the objective variable and the factor variable that is not directly adjacent to the objective variable (step S192).

The intervention unit 235 then looks to see if there is a factor variable with the intervention flag (step S195) and, if so, obtains the intervention method from the intervention rule DB226 (step S198). The intervention unit 235 may determine whether or not to intervene in the factor variable with the intervention flag. For example, the "wake-up time" is flagged as intervening, and the objective variable ("group time") is set to the target range (for example, "3 hours or more") (solving the problem that "group time" is short). In addition, if you want to set the "wake-up time" to "7:30", the intervention unit 235 acquires the tendency of the user's usual wake-up time, and if it tends to wake up at 9 o'clock, it wakes up at 7:30. Judge that "intervention should be done".

Next, a device having the ability required for intervention is searched from the intervention device DB (step S201), and if a corresponding device is found (step S204 / Yes), the intervention condition and command / parameter are displayed in the intervention reservation DB 228. Is registered (step S207). For example, if the alarm function of the user's smartphone can be controlled, an intervention reservation such as "set the alarm of the user's smartphone at 7:30" is made.

(2-2-4. Intervention processing)
FIG. 16 is a flowchart showing an intervention process by the output device 36. As shown in FIG. 16, the output device 36 waits for a command reception from the intervention unit 235 of the information processing device 20 (step S213).

The output device 36 then parses the received commands and parameters (step S216) and selects the presentation method corresponding to the commands (step S219).

Then, the output device 36 executes the command (step S222).

<2-3. Supplement>
In the first embodiment described above, the problem related to family relations has been used as an example, but the present embodiment is not limited to this, and for example, "difference in values" may exist as a problem. As the relationship of the factors for detecting the difference in values (estimating the problem), for example, the items shown in Table 5 below can be considered.

In Table 5 above, the "separation line segment of an object" is a feature that the contour line density is low and the individual contour lines are long when the image of a cluttered room and the image of an organized room are compared. There is. Therefore, in the image analysis of each user's room or living room, it is possible to calculate the degree of clutter based on, for example, the separation line segment of the object.

FIG. 17 is a diagram showing an example of a causal path of differences in values. As shown in FIG. 17, when "difference in values" is set in the objective variable 2011 and causal analysis is performed, for example, causal variable 2013 "room tidying rate" and causal variable 2014 "time until opening of mail" , Causal variable 2015 "Percentage of time spent at home in a day", Causal variable 2016 "Number of drinking parties per month", Causal variable 2017 "Number of golfs per month", etc. are raised on the causal path.

As an intervention method in this case, for example, the items shown in Table 6 below can be considered.

As a result, the intervention unit 235 increases the time spent at home and raises the "room tidying rate" related to the time spent at home, for example, by notifying the user to reduce the number of drinking parties and the number of golfs. As a result, it is possible to eliminate differences in family values (such as a room with a high degree of clutter).

<< 3. Second Example (Problem Estimate and Behavior Change) >>
Next, the master system 10-2 according to the second embodiment will be described with reference to FIGS. 18 to 28.

In this embodiment, based on data collected from a specific community such as a family or a small group (company, school, neighborhood association, etc.), the standard values (value standards) within that community are used as the code of conduct. It is automatically generated and indirectly encourages behavior change (behavior that approaches the value standard) for members who deviate significantly from the value standard (more than a certain distance).

FIG. 18 is a block diagram showing an example of the configuration of the master system 10-2 according to the second embodiment. As shown in FIG. 18, the master system 10-2 includes an information processing device 50, a sensor 60 (or a sensor system), and an output device 62 (or an output system).

(Sensor 60)
The sensor 60 is similar to the sensor group according to the first embodiment, and is a device / system that acquires all information about the user. For example, environment-side sensors such as cameras and microphones installed in rooms, motion sensors (accelerometers, gyro sensors, geomagnetic sensors), biosensors, position sensors, cameras, and microphones installed in user-owned smartphones and wearable devices. Various sensors on the user side such as are included. Further, the user's action history (movement history, SNS, shopping history, etc.) may be acquired from the network. The sensor 60 senses the behavior of members in the specific community on a daily basis and collects it in the information processing device 50.

(Output device 62)
The output device 62 is a display device that promotes behavior change, and is a smartphone, a tablet terminal, a mobile phone terminal, a PC, a wearable device, a TV, a lighting device, a speaker, a vibration device, or the like, as in the first embodiment. A wide range of IoT devices can be included.

(Information processing device 50)
The information processing device 50 (value presentation server) has a communication unit 510, a control unit 500, and a storage unit 520. The information processing device 50 may be configured by a cloud server on the network, an intermediate server or an edge server, or a dedicated terminal placed in a home such as a home agent. It may be composed of an information processing terminal such as a PC or a smartphone.

・ Control unit 500
The control unit 500 functions as an arithmetic processing unit and a control device, and controls the overall operation in the information processing device 50 according to various programs. The control unit 500 is realized by an electronic circuit such as a CPU (Central Processing Unit) or a microprocessor. Further, the control unit 500 may include a ROM (Read Only Memory) for storing programs to be used, calculation parameters, and the like, and a RAM (Random Access Memory) for temporarily storing parameters and the like that change as appropriate.

In addition, the control unit 500 according to the present embodiment also functions as a user management unit 501, a value estimation unit 502, a value comparison unit 503, and a presentation unit 504.

The user management unit 501 manages information for identifying the user and each user's sense of values for the target action / thing, and stores the information in the storage unit 520 as appropriate. Various indicators are assumed for the values, and Table 7 below shows an example of the values used in this embodiment. Further, in this embodiment, the sensing behavior (necessary information) for estimating each value may be defined in advance as follows.

The value estimation unit 502 automatically estimates (generates) a value standard (hereinafter, also referred to as a standard value) determined within a target behavior / thing group (specific community), and stores it in the storage unit 520. In addition, the value estimation unit 502 also estimates and manages the individual values of the user. The standard value of the group may be, for example, the average value of the values of each user in the group (may be calculated by weighting each member), or the value of a specific user (for example, relatives). May be the standard value of the group. What kind of information is used to estimate each value may be defined in advance as shown in Table 8 below, for example.

The value comparison unit 503 detects the deviation of each user's value from the standard value of behavior / thing that is sensed on a daily basis. The standard values in the group may be automatically generated by the value estimation unit 502 as described above, or may be preset (default settings may be made on the system side, or the group user may manually set them. May be set with).

The presentation unit 504 promotes behavior change that approaches the standard values of the group when the values deviate. Specifically, the presentation unit 504 transmits a behavior change command from the communication unit 510 to the output device 62.

(Communication unit 510)
The communication unit 510 connects to an external device such as a sensor 60 or an output device 62 by wire or wirelessly to transmit / receive data. The communication unit 510 is, for example, a wired / wireless LAN (Local Area Network), Wi-Fi (registered trademark), Bluetooth (registered trademark), a mobile communication network (LTE (Long Term Evolution)), or 3G (third generation mobile body). Communication connection with an external device by means of communication method)).

(Memory unit 520)
The storage unit 520 is realized by a ROM (Read Only Memory) that stores programs and arithmetic parameters used for processing of the control unit 500, and a RAM (Random Access Memory) that temporarily stores parameters that change as appropriate.

The configuration of the master system 10-2 according to this embodiment has been specifically described above.

<3-2. Operation processing>
Subsequently, the operation processing of the master system 10-2 described above will be described with reference to the flowchart.

(3-2-1. Basic flow)
FIG. 19 is a basic flowchart of the operation process according to the present embodiment. As shown in FIG. 19, first, the information processing device 50 collects and analyzes the behavior of each member in the group and the sensing information of the object (step S303) (step S306).

Next, when the behavior and the thing related to the value can be sensed (step S309 / Yes), the information processing apparatus 50 registers the information about the target action and the value of the thing (step S312).

Next, the information processing apparatus 50 calculates the reference value (of the group) and estimates the individual value (individual value) (step S315). For the calculation of the standard value, for example, the average value of the values of each member of the group may be calculated, or the value of someone in the member may be used as the standard value.

Subsequently, when the member deviates from the standard value (step S318 / Yes), the information processing apparatus 50 performs a presentation process for urging the member to change his / her behavior (step S321). For example, if the member's values (individual values) deviate from the (group's) standard values, a predetermined UI that encourages behavior change is presented. The information presentation that promotes such behavior change may be a specific instruction for eliminating the deviation from the standard values, or the content that encourages behavior change may be presented casually.

The above basic flow will be described below with reference to specific examples. In the following, the information presentation process of behavior change will be specifically described using specific examples of values.

(3-2-2. Food values)
First of all, as values related to "food", "discipline of food" such as valuing food and not leaving food is assumed. In this embodiment, when the values of food deviate from such values, a presentation is made to encourage the target members to change their behavior.

FIG. 20 is a flowchart showing a behavior change process related to food discipline according to this embodiment. As shown in FIG. 20, first, the information processing apparatus 50 observes the behavior of eating by using a camera or the like (step S333) and analyzes the sensor data (step S336).

Next, when an event such as leaving / not leaving a meal is detected (step S339 / Yes), the information processing apparatus 50 records an action regarding whether or not the meal is left (step S342).

Next, when the information processing device 50 detects a predetermined number of times that the member has left even though there is food left (step S345 / Yes), information presentation prompting not to leave food, for example, for children, We will present a video showing the importance of ingredients (rice and vegetables) using characters. In this embodiment, the number of times a meal is left is estimated as an individual value, and the value of the majority of the entire group (the standard value of the group that serves as a code of conduct. For example, if the majority of the group does not leave a meal each time, each time. If an action different from the standard value of this group is performed a predetermined number of times, it is judged that there is a deviation from the standard value. The image may be presented to the target child's smartphone or wearable device, or may be projected on a table by a projector. In addition, it may be output by voice such as "Don't leave!", "One grain is a thousand grains", and the auditory AR that sounds like the remaining ingredients are speaking.

(3-2-3. Values of housework)
In addition, as a sense of value related to housework, for example, it is assumed that "the whole family cleans up the dishes after meals". If the values deviate from such household chores, a presentation is made to encourage the target members to change their behavior.

FIG. 21 is a flowchart showing a behavior change process related to cleaning up the dishes according to the present embodiment. As shown in FIG. 21, first, the information processing apparatus 50 observes the behavior of eating by using a camera or the like (step S353), and analyzes the sensor data (step S356).

Next, when the event of lowering / not lowering the tableware after eating is detected (step S359 / Yes), the information processing device 50 records the behavior data of whether or not each member lowered the tableware (step S362).

Next, when the information processing device 50 detects that the member has left without cleaning the dishes a predetermined number of times (step S365 / Yes), the information processing device 50 presents information prompting the members to clean the dishes, for example, for children, the tableware is displayed by auditory AR. You may output a sound that sounds like whispering, "I want to get clean quickly." In this embodiment, the number of times the dishes are cleaned up is estimated as individual values, and the values of the majority of the entire group (the standard values of the group that serve as the code of conduct. For example, if the majority of the group cleans up each time, clean up each time. If an action different from the standard value of this group is performed a predetermined number of times, it is judged that there is a deviation from the standard value.

(3-2-4. The beauty of the room)
In addition, as a value related to the aesthetics of a room such as an office or one's own room, for example, the degree of tidying up (degree of tidying up) is assumed, such as no objects being scattered on the floor or desk.

FIG. 22 is a flowchart showing a behavior change process related to tidying up an office desk. As shown in FIG. 22, for example, the information processing device 50 detects (imaging) the number of objects placed on the desk in the office using a camera or the like (step S373), analyzes sensor data (calculates the number of objects by image analysis, etc.). ) (Step S376). Here, "the number of objects" is used as an example, but the degree of organization may be detected by image analysis.

Next, when the status of all the members in the office can be detected (step S379), the information processing apparatus 50 registers the average number of groups as a reference (standard value) and puts it on the desk of each member. The number of objects is also recorded for calculating individual values (step S382).

Next, when the number of objects placed on the member's desk is larger than the average of the group (step S385 / Yes), the information processing device 50 presents information prompting the member to clean up the desk, for example, the pile of documents is raised and is about to collapse. You may indirectly indicate that it is better to organize by projecting projection mapping or pinpointing piles of documents. The information presentation prompting the desk to be tidied up may be presented by voice such as auditory AR.

FIG. 23 is a flowchart showing a behavior change process related to tidying up the room. As shown in FIG. 23, first, the information processing device 50 uses a camera or the like to detect the number of objects scattered (falling) on the floor and the remarks that the child is angry with the mother with a microphone or the like (step S393). , The sensor data is analyzed (step S396).

Next, when the child is angry with the mother regarding the state of the room (step S399 / Yes), the information processing device 50 considers the number of things falling on the floor as the mother's limit and registers it as the standard value of the group (step S399 / Yes). Step S402). In this example, regarding the aesthetics of the room, the mother's limits are defined as the group's value standard. The aesthetics of a room are not limited to the number of things that have fallen, for example, the ratio of the floor area of the room (a situation where there is no foothold can be said to be a messy situation), and the usual state of the room (floor area and The value standard may be defined by sensing the difference from the ordering condition, etc.).

Then, when the condition of the room exceeds the mother's standard (that is, when the number of things falling in the room exceeds the mother's standard "number of falling things" which is the standard value of the group) (step S405). / Yes), presenting information that encourages the room to be tidied up, eg, projecting projection mapping that makes the room appear more cluttered, as shown in FIG. In the example shown in FIG. 24, the number of objects falling on the floor is detected by a sensor 60 such as a camera installed in the room, and if the number exceeds the standard, the output device 62 such as a projector makes the image 620 look more cluttered. Is projected by projection mapping. Information that prompts the room to be tidied up may be presented by voice such as auditory AR.

In addition, a video of the current situation of the room from the child's perspective may be presented to the parents. It is also possible to map the clutter of the room to other values such as emotions and present it to the child. For example, if the room is dirty, the hero associated with the room will be shown as weakened or uncool.

(3-2-5. Values of childcare)
In addition, as a value regarding childcare, for example, it is common for mothers to immediately notice and wake up when a baby cries at night, but for fathers to react slowly. Therefore, one example is to define the mother's tolerance level for baby crying (which must be awakened) as the group's standard values and encourage the father to change behavior.

FIG. 25 is a flowchart showing a behavior change process related to the baby's crying. As shown in FIG. 25, for example, the information processing device 50 detects the crying volume level when the mother wakes up with the baby's crying using a camera, a microphone, or the like (step S413), and analyzes the sensor data (step S416). ).

Next, when the mother wakes up to take care of the baby (step S419 / Yes), the information processing device 50 refers to the crying volume level when the mother wakes up (the mother's permissible level, which is a group). (As the standard value of) (step S422).

Then, when the baby's cry exceeds the wife's permissible level (that is, the group's standard values) (step S425 / Yes), the information processing device 50 amplifies the information presentation urging the father, for example, the baby's cry. Present the auditory AR to the father (step S428).

(3-2-6. Values for things)
Also, as a value regarding love for things, for example, a certain stuffed animal is very important for a child, but from the perspective of a mother, all stuffed animals are treated the same. In this embodiment, when the difference between the child's and mother's values for things (such as a stuffed animal) exceeds a certain level, the child's values are visualized to encourage the mother to (indirectly) change their behavior. Make it possible.

FIG. 26 is a flowchart showing a behavior change process relating to a toy. As shown in FIG. 26, first, the information processing device 50 senses the frequency of use of the toy, the behavior of the toy, the handling of the toy, etc. by a camera, a microphone, or the like (step S433), and analyzes the sensor data (step). S436). Specifically, for example, it is possible to measure the frequency of use (frequency of children playing with the toy) by using the approach of radio waves such as camera images and BLE / RFID (transmitted from the toy). .. You may also collect conversations with a microphone and extract and count remarks about which toys are important, which toys are fun to play with, which toys you like, and so on. It is also possible to measure the handling of toys (whether they are handled carefully or roughly) using camera images, conversational voices from microphones, and radio waves such as BLE / RFID.

Next, when the child's attachment to the toy (such as the degree of affection) is high (step S439 / Yes), the information processing device 50 registers the toy as an important toy (toy with a high degree of affection) (step S442). ).

Next, when it is time for the mother to sort out the toys (step S445 / Yes), for example, when the toy that the mother is trying to throw away is a toy with a high degree of affection for the child, information on the child's values is presented to the mother. For example, an image of a child cherishing a toy is presented to a mother's smartphone or the like (step S448). The timing for the mother to sort out the toys is determined by, for example, analysis of the camera image and analysis of the sound collected by the microphone (statements such as "There are too many toys, so I'll sort them out" and "I'll throw them away"). It is possible. In addition, which toys the mother is trying to throw away can be detected, for example, by analyzing the camera image or analyzing the radio waves transmitted from tags such as BLE / RFID provided on the toys (the toys are thrown into a trash box or the like and can be detected. It can be judged based on such things as disappearing).

(3-2-7. Comprehensive values)
Next, as an example of values, the values (comprehensive values) of what kind of values are emphasized are also assumed. The values (base values) that are the basis for calculating the total values are, for example, the above-mentioned "value meals", "help the whole family with housework", and "aesthetics (room tidy state)". , "Childcare", "love for things", etc. Based on these base values, we estimate the values (that is, "comprehensive values") of what values each member attaches importance to (candidates for total values), for example, a group. The average of is the total value. Then, when there is a discrepancy between the overall values of the group and the overall values of the individual (member), behavior change (matching with the overall values of the group, etc.) by presenting the overall values of the group to the members, etc. ) Can be prompted.

FIG. 27 is a flowchart showing the behavior change process related to the comprehensive values. As shown in FIG. 27, first, the information processing apparatus 50 estimates the base values of an individual (each member of the group) (step S453).

Next, the information processing device 50 normalizes the value of the individual base value (step S456).

Next, the information processing apparatus 50 refers to the related table of the values and calculates the value for each total value according to the weighted value of the associated total values (step S459). Here, an example of a related table of values is shown in Table 9 below. As shown in Table 9 below, candidates for comprehensive values include, for example, "honesty," "compassion," "society," and "personality."

Next, the information processing apparatus 50 sets the highest value (that is, the most important value) as the total value (step S462). Here, FIG. 28 shows an example of the value for each total value of each member calculated with reference to the weighting shown in Table 9 above. In the example shown in FIG. 28, since the value of the value of "compassion" is the highest, this value becomes the value that the member places the highest importance on, that is, the "total value" of the member.

Then, when the total values of the members deviate from the total values of the group average (step S465 / Yes), the information processing apparatus 50 presents the change of the total values to the members (step S468).

<< 4. Third Example (Problem Estimate and Behavior Change) >>
Next, the master system 10-3 according to the third embodiment will be described with reference to FIGS. 29 to 35.

In this embodiment, for example, when the problem of insufficient grouping time is estimated based on the data collected from the family (the problem estimation is the same as in the first example), in order to solve the problem. The code of conduct is to match the meal time zone, detect the family life rhythm (dining time zone of each member, etc.), and indirectly promote the behavior change of the life rhythm so that each person's meal time matches.

FIG. 29 is a block diagram showing an example of the configuration of the master system 10-3 according to the third embodiment. As shown in FIG. 29, the master system 10-3 includes an information processing device 70, a sensor 80 (or a sensor system), and an output device 82 (or an output system).

(Sensor 80)
The sensor 80 is similar to the sensor group according to the first embodiment, and is a device / system that acquires all information about the user. For example, environment-side sensors such as cameras and microphones installed in rooms, motion sensors (accelerometers, gyro sensors, geomagnetic sensors), biosensors, position sensors, cameras, and microphones installed in user-owned smartphones and wearable devices. Various sensors on the user side such as are included. Further, the user's action history (movement history, SNS, shopping history, etc.) may be acquired from the network. The sensor 60 senses the behavior of members in the specific community on a daily basis and collects it in the information processing device 70.

(Output device 82)
The output device 82 is a display device that promotes behavior change, and is a smartphone, a tablet terminal, a mobile phone terminal, a PC, a wearable device, a TV, a lighting device, a speaker, a vibration device, or the like, as in the first embodiment. A wide range of IoT devices can be included.

(Information processing device 70)
The information processing device 70 (life rhythm derivation server) has a communication unit 710, a control unit 700, and a storage unit 720. The information processing device 70 may be configured by a cloud server on the network, an intermediate server or an edge server, or a dedicated terminal placed in a home such as a home agent. It may be composed of an information processing terminal such as a PC or a smartphone.

・ Control unit 700
The control unit 700 functions as an arithmetic processing unit and a control device, and controls the overall operation in the information processing device 70 according to various programs. The control unit 700 is realized by an electronic circuit such as a CPU (Central Processing Unit) or a microprocessor. Further, the control unit 700 may include a ROM (Read Only Memory) for storing programs to be used, calculation parameters, and the like, and a RAM (Random Access Memory) for temporarily storing parameters and the like that change as appropriate.

Further, the control unit 700 according to the present embodiment also functions as a person recognition unit 701, a motion recognition unit 702, a rhythm derivation unit 703, a separation detection unit 704, a separation cause estimation unit 705, and a response generation unit 706.

The person recognition unit 701 recognizes a person by face recognition of a camera image or the like. The motion recognition unit 702 recognizes each user's motion (going home, eating, bathing, relaxing time, going to bed, etc.) based on the camera image and various sensor data. Specifically, for example, a home sensor (camera, microphone, etc.) senses the family's return time, meal time, etc., and records the return time, meal time, and the like. In addition, as shown in FIG. 30, a record of the people who ate together and the number of people is also recorded.

The rhythm derivation unit 703 calculates the family life rhythm (for example, the tendency of returning home time, meal time, bathing time, etc. for each day of the week) based on the above-mentioned family behavior record.

The separation detection unit 704 compares the life rhythms of each member of the family and detects the separation portion. For example, if the rhythm of the family's supper time is significantly different from the father's supper time, the separation is detected.

The separation cause estimation unit 705 estimates the separation cause. For example, if only the father's supper time is significantly off, the cause of the father's supper time is estimated. Examples of the cause estimation include a method of causal analysis or a method using Bayesian estimation. For example, if you cannot have dinner with your family because you come home late every Thursday, you can presume that the late return time is due to the company's regular meetings every Thursday.

The response generation unit 706 indirectly generates a response that promotes behavior change so as to match the rhythm of life. For example, as mentioned above, if only the father often cannot have dinner together on Thursday, it can be analyzed that the cause is the regular meeting, so advice such as "What if I change the regular meeting on Thursday?" Is given on the PC screen. Present it to the father. By following this advice, you will eventually be able to have dinner with your family. The presentation of advice may be transmitted using a message function such as e-mail or SNS.

Here, FIG. 31 shows a diagram for explaining the difference in life rhythm. FIG. 31 shows the meal times for dinner for fathers, mothers, and children, and the standard life rhythm (code of conduct) for families. The standard life rhythm of a family is, for example, the cumulative average time of the family's dinner time for each day of the week. As shown in FIG. 31, when the life rhythms of the father, mother, and child's dinner meal times are calculated, it can be seen that only the father's meal times on Thursday are far apart. In this case, it can be seen from the cause estimation that, for example, due to a regular meeting, the return home is delayed and the meal time is shifted. On the other hand, it turns out that the family dinner time on Tuesday is later than the other days. In this case, by changing the regular meeting to Tuesday, it is possible to have dinner together even if the regular meeting delays the return home because Thursday is an early meal time and Tuesday is a late meal time. There is. Therefore, the response generation unit 706 can generate specific advice such as "Why don't you change the regular meeting on Thursday to Tuesday?" The image 7001 including the graph and advice shown in FIG. 31 may be presented to the father as advice.

(Communication unit 710)
The communication unit 710 connects to an external device such as the sensor 80 or the output device 82 by wire or wirelessly to transmit / receive data. The communication unit 710 is, for example, a wired / wireless LAN (Local Area Network), or Wi-Fi (registered trademark), Bluetooth (registered trademark), mobile communication network (LTE (Long Term Evolution)), 3G (third generation mobile body). Communication connection with an external device by means of communication method)).

(Memory unit 720)
The storage unit 720 is realized by a ROM (Read Only Memory) that stores programs and arithmetic parameters used for processing of the control unit 700, and a RAM (Random Access Memory) that temporarily stores parameters that change as appropriate.

The configuration of the master system 10-3 according to this embodiment has been specifically described above.

<4-2. Operation processing>
Subsequently, the operation processing of the master system 10-3 described above will be described with reference to the flowchart.

FIG. 32 is a flowchart showing an operation process for generating a rhythm of dinner time. As shown in FIG. 32, first, the information processing apparatus 70 recognizes a person at the dining table by a camera or the like (step S503), and recognizes that the person is “during a meal” by motion analysis (step S506).

Next, when it is possible to recognize who is eating (step S509 / Yes), the information processing apparatus 70 records the dinner time of the family members (step S512). Table 10 below shows an example of a record of dinner times for family members.

Then, when the (common sense of the day) supper time is over (step S515 / Yes), the information processing apparatus 50 adds the data of today's family supper time to the past average supper time, and for each day. The cumulative average time is calculated (the rhythm of the dinner time is generated) (step S518). Here, an example of the calculation formula of the cumulative average time for each day of the week is shown in FIG. 33. For example, the cumulative average time for each day of the week, that is, the standard life rhythm of the family shown in FIG. 31, may be calculated by the calculation formula shown in FIG.

FIG. 34 is a flowchart for generating advice based on the rhythm of life. As shown in FIG. 34, first, the information processing device 70 detects the deviation of the family life rhythm. Specifically, for example, the information processing apparatus 70 calculates an average square error between the dinner time of a member in the past fixed period (for example, 3 months) and the cumulative average time for each day of the week (step S523).

Next, when the calculated error exceeds a predetermined threshold value (step S526 / Yes), the information processing device 70 estimates the reason why the supper time deviates from the family supper time (cause of the separation) and promotes behavior change. The indirect representation is selected (step S529). The cause of the separation may be estimated by, for example, a method of causal information analysis.

Then, the information processing device 70 sends a message in the selected indirect expression (step S532).

In addition, the behavior change for adjusting the supper time is not limited to the urge only to the father who is separated from the supper time, but to urge other families so that the whole family can have dinner as a result. It may be. For example, it transforms the family's life rhythm so that the family is near the nearest station when the father returns home. Specifically, for example, based on the life rhythm shown in Fig. 31, the master system 10-3 tells the mother "If you go out to the station with your child" on Thursday evening, "A shop called XX in front of the station It seems to be a topic. " When the mother was shopping in front of the station with her child according to the master's words, the father on the way home contacted me, "It's about to be the station," and the mother said, "Oh, I'm just near the station." The family will join naturally by replying, and everyone will be able to have dinner at a restaurant near the station.

Further, in this embodiment, "dinner time" is taken as an example of the rhythm of life, but the present disclosure is not limited to this, and for example, wake-up time, bedtime, working time, exercise time, media viewing time, etc. Is also assumed.

<4-3. Modification example>
In the above-described embodiment, the purpose is to match the life rhythm, but the present disclosure is not limited to this, and for example, indirectly advice may be given so that the life rhythm shifts (asynchronously). For example, it is preferable that the bathing time, the toilet time, and the time for using the wash basin (dressing) are different.

The information processing device 70 has a knowledge table of events and the like that occur when the life rhythms are aligned, as shown in Table 11 below, and refers to the table below to change the behavior of the community members by intentionally shifting the life rhythms. Indirectly advise.

As an assumed scene, for example, it is premised that the master system 10-3 records the situation by routinely sensing the behavior of family members by sensors such as cameras and microphones installed in the house. At this time, for example, the situation where most of the family (father, mother, eldest daughter, etc.) gathers in the living room and watches TV is detected, and it has already been found that the bathroom tends to be crowded after watching TV. If you know (you can learn and get it, or you can pre-register it, see Table 11), Master System 10-3 tells the eldest son, who is alone in his room without watching TV, "now. Then, I'll take a bath slowly. " Notification can be performed by various output devices such as smartphones, projectors, and speakers. The eldest son, who was studying in his room, said, "It was just right. I wanted to relax in the bath," and I can take a bath with a break in my studies.

FIG. 35 is a flowchart that promotes adjustment (behavior change) of the life rhythm according to the duplication of events according to the modified example of this embodiment. As shown in FIG. 35, first, the information processing device 70 recognizes the state (behavior) of the family (step S543), and causes an action (event) registered in the table as shown in Table 11 above. It is determined whether or not it is an action (step S546). For example, it is assumed that the wake-up times of many people overlap, the TV viewing time overlaps, and the like.

Next, when the duplication of actions is equal to or greater than the threshold value (step S549 / Yes), the information processing apparatus 70 selects the advise target person (step S552). You may choose from those who are taking duplicate actions, or you may choose from those who are not taking duplicate actions. The conditions under which the target person is selected may be registered in advance for each expected event as shown in Table 11.

Then, the information processing device 70 executes the advice registered in the table (table) to the selected target person (step S555).

In the example described above, as described above, it is assumed that a large number of cameras and microphones are installed in the house to grasp the situation of the family on a daily basis, but a large number of cameras are installed in the house. Even if a microphone or microphone is not installed, it is possible to grasp the information based on information from, for example, a camera, a microphone, a motion sensor, or the like of a smartphone or smart band owned by the user.

For example, it is possible to sense watching TV and dinner time with a smartphone camera or microphone. In addition, location information such as which room you are in can be obtained from the radio waves of your smartphone (this allows you to roughly predict what you are doing if you know the location such as the bathroom, toilet, and living room).

In addition, whether or not you are in the toilet is determined by, for example, the sound of flowing water being detected by the microphone of the smartphone brought into the toilet, or by detecting that the echo is large and the echo interval is short, a small enclosed space. That is, it is possible to determine that the person is in the toilet (or bathroom).

In addition, whether the user has taken a bath before or after taking a bath can be determined from the appearance of the user (wet hair, wearing pajamas, wearing a hair dryer, etc.) in the camera of the smartphone.

<< 5. Hardware configuration >>
Finally, with reference to FIG. 36, the hardware configuration of the information processing apparatus according to the present embodiment will be described. FIG. 36 is a block diagram showing an example of the hardware configuration of the information processing device 20, the information processing device 50, or the information processing device 70 according to the present embodiment. The information processing device 800 shown in FIG. 36 can realize, for example, the information processing device 20, the information processing device 50, or the information processing device 70. The information processing by the information processing device 20, the information processing device 50, or the information processing device 70 according to the present embodiment is realized by the cooperation between the software and the hardware described below.

As shown in FIG. 36, the information processing apparatus 800 includes, for example, a CPU 871, a ROM 872, a RAM 873, a host bus 874, a bridge 875, an external bus 876, an interface 877, an input device 878, and an output device 879. It has a storage 880, a drive 881, a connection port 882, and a communication device 883. The hardware configuration shown here is an example, and some of the components may be omitted. Further, components other than the components shown here may be further included.

(CPU871)
The CPU 871 functions as, for example, an arithmetic processing device or a control device, and controls all or a part of the operation of each component based on various programs recorded in the ROM 872, the RAM 873, the storage 880, or the removable recording medium 901.

Specifically, the CPU 871 realizes the operation processing in the information processing device 20, the information processing device 50, or the information processing device 70.

(ROM872, RAM873)
The ROM 872 is a means for storing a program read into the CPU 871 and data used for calculation. The RAM 873 temporarily or permanently stores, for example, a program read into the CPU 871 and various parameters that change as appropriate when the program is executed.

(Host bus 874, Bridge 875, External bus 876, Interface 877)
The CPU 871, ROM 872, and RAM 873 are connected to each other via, for example, a host bus 874 capable of high-speed data transmission. On the other hand, the host bus 874 is connected to the external bus 876, which has a relatively low data transmission speed, via, for example, the bridge 875. Further, the external bus 876 is connected to various components via the interface 877.

(Input device 878)
For the input device 878, for example, a mouse, a keyboard, a touch panel, buttons, switches, levers, and the like are used. Further, as the input device 878, a remote controller (hereinafter, remote controller) capable of transmitting a control signal using infrared rays or other radio waves may be used. Further, the input device 878 includes a voice input device such as a microphone.

(Output device 879)
The output device 879 provides the user with acquired information such as a display device such as a CRT (Cathode Ray Tube), an LCD, or an organic EL, an audio output device such as a speaker or a headphone, a printer, a mobile phone, or a facsimile. It is a device that can notify visually or audibly. Further, the output device 879 according to the present disclosure includes various vibration devices capable of outputting a tactile stimulus.

(Storage 880)
The storage 880 is a device for storing various types of data. As the storage 880, for example, a magnetic storage device such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, an optical magnetic storage device, or the like is used.

(Drive 881)
The drive 881 is a device that reads information recorded on a removable recording medium 901 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, or writes information on the removable recording medium 901.

(Removable recording medium 901)
The removable recording medium 901 is, for example, a DVD media, a Blu-ray (registered trademark) media, an HD DVD media, various semiconductor storage media, and the like. Of course, the removable recording medium 901 may be, for example, an IC card equipped with a non-contact type IC chip, an electronic device, or the like.

(Connection port 882)
The connection port 882 is a port for connecting an external connection device 902 such as a USB (Universal Serial Bus) port, an IEEE1394 port, a SCSI (Small Computer System Interface), an RS-232C port, or an optical audio terminal. is there.

(External connection device 902)
The externally connected device 902 is, for example, a printer, a portable music player, a digital camera, a digital video camera, an IC recorder, or the like. The externally connected device 902 may be, for example, an environment side sensor 30, a user side sensor 32, an output device 36, a sensor 60, an output device 62, a sensor 80, and an output device 82.

(Communication device 883)
The communication device 883 is a communication device for connecting to a network, for example, a communication card for wired or wireless LAN, Wi-Fi (registered trademark), Modem (registered trademark), or WUSB (Wireless USB), optical communication. A router for ADSL (Asymmetric Digital Subscriber Line), a modem for various communications, and the like. For example, the service server 34 shown in FIG. 5 may be connected via the communication device 883.

<< 6. Summary >>
As described above, in the information processing system according to the embodiment of the present disclosure, it is possible to automatically generate a code of conduct for the community and promote spontaneous behavior change.

Although the preferred embodiments of the present disclosure have been described in detail with reference to the accompanying drawings, the present technology is not limited to such examples. It is clear that a person having ordinary knowledge in the technical field of the present disclosure can come up with various modifications or modifications within the scope of the technical ideas described in the claims. Of course, it is understood that the above also belongs to the technical scope of the present disclosure.

For example, the information processing device 20, the information processing device 50, or the information processing device 70 is equipped with hardware such as a CPU, ROM, and RAM built in the information processing device 20, the information processing device 50, or the information processing device 70 described above. It is also possible to create a computer program to demonstrate the functions of. Also provided is a computer-readable storage medium that stores the computer program.

In addition, the effects described herein are merely explanatory or exemplary and are not limited. That is, the techniques according to the present disclosure may exhibit other effects apparent to those skilled in the art from the description herein, in addition to or in place of the above effects.

The present technology can also have the following configurations.
(1)
Acquire sensor data obtained by sensing members belonging to a specific community,
Based on the acquired sensor data, the code of conduct in the specific community is automatically generated.
Encourage the members to change their behavior based on the code of conduct,
An information processing device including a control unit that performs control.
(2)
The control unit
Based on the acquired sensor data, the issues faced by the specific community are estimated, and
The information processing device according to (1) above, which automatically generates the code of conduct for solving the problem.
(3)
The information processing device according to (1) above, wherein the control unit indirectly urges the member to change the behavior so that the member changes the behavior.
(4)
The control unit
Set the objective variable as the code of conduct,
A relationship graph showing the relationship between factor variables starting from the objective variable is generated.
The information processing apparatus according to (3) above, wherein among the factor variables related to the objective variable, the information processing apparatus according to (3) above urges the member to change the behavior of the factor variable to be intervened, which can change the behavior.
(5)
The control unit
The information processing apparatus according to (4) above, which works on the member so that the factor variable related to the objective variable approaches a desired value.
(6)
The control unit
A causal graph is generated by estimating the factor variable that is presumed to be the cause of the objective variable set as the code of conduct.
The information processing apparatus according to (4) above, which works on the members so that the factor variable presumed to be the cause of the objective variable approaches a desired value.
(7)
The control unit
Based on the acquired sensor data, the standard values in the specific community are automatically generated as a code of conduct.
The information processing device according to (3) above, which indirectly encourages the members to change their behavior based on the reference values.
(8)
The control unit
The information processing device according to (7) above, wherein the average value of the values of a plurality of members belonging to the specific community is used as a reference value.
(9)
The control unit
The information processing device according to (7) above, wherein the values of a specific member among a plurality of members belonging to the specific community are set as reference values.
(10)
The control unit
Any of the above (7) to (9) that indirectly promotes behavior change by presenting the standard value to a specific member who is separated from the standard value by a certain amount or more. The information processing apparatus according to item 1.
(11)
The control unit
Based on the acquired sensor data, the issues faced by the members belonging to the specific community are estimated.
The information processing device according to any one of (2) to (6) above, which automatically generates a code of conduct related to the life rhythm of a member belonging to the specific community in order to solve the problem.
(12)
The control unit
The information processing device according to (11) above, which automatically generates a code of conduct that synchronizes the living rhythms of a plurality of members belonging to the specific community in order to solve the problem.
(13)
The control unit
The information processing device according to (12) above, which indirectly encourages a specific member who is separated from another member belonging to the specific community for a specific life rhythm for a certain period of time or more.
(14)
The control unit
The information processing device according to (11) above, which automatically generates a code of conduct that asynchronizes the life rhythms of a plurality of members belonging to the specific community in order to solve the problem.
(15)
The control unit
In the first living behavior, when it is detected that a certain number or more of the plurality of members belonging to the specific community are synchronized, the second living behavior that is expected to come after the first living behavior is performed. The information processing device according to (14), wherein the behavioral change is indirectly promoted to a specific member belonging to the specific community.
(16)
According to the code of conduct in the specific community that was automatically generated in advance based on the sensor data obtained by sensing the members belonging to the specific community.
According to the sensor data obtained by sensing the member belonging to the specific community.
Encourage the members to change their behavior,
An information processing device including a control unit that performs control.
(17)
The processor
Acquiring sensor data obtained by sensing members belonging to a specific community,
To automatically generate a code of conduct in the specific community based on the acquired sensor data,
Encouraging the members to change their behavior based on the code of conduct,
Information processing methods, including.
(18)
Computer,
Acquire sensor data obtained by sensing members belonging to a specific community,
Based on the acquired sensor data, the code of conduct in the specific community is automatically generated.
Encourage the members to change their behavior based on the code of conduct,
A recording medium in which a program for functioning as a control unit for controlling is recorded.

2A-2C Community 10, 10A-10C, 10-1-10-3 Master system 11 Data analysis unit 12 Code of conduct generation unit 13 Behavior change instruction unit 20 Information processing device (causal analysis server)
30 Environment side sensor 32 User side sensor 34 Service server 36 Output device 50 Information processing device 70 Information processing device 80 Sensor 82 Output device 201 Reception unit 203 Transmission unit 210 Image processing unit 212 Voice processing unit 214 Sensor / behavior data processing unit 220 Factor Variable DB
222 Problem index DB
224 Intervention device DB
226 Intervention rule DB
228 Intervention reservation DB
230 Problem estimation unit 232 Causal analysis unit 235 Intervention unit 500 Control unit 501 User management unit 502 Value estimation unit 503 Value comparison unit 504 Presentation unit 510 Communication unit 520 Memory unit 700 Control unit 701 Person recognition unit 702 Motion recognition unit 703 Rhythm Derivation unit 704 Disengagement detection unit 705 Disengagement cause estimation unit 706 Response generation unit 710 Communication unit 720 Storage unit

Claims (18)

Acquire sensor data obtained by sensing members belonging to a specific community,
Based on the acquired sensor data, the code of conduct in the specific community is automatically generated.
Encourage the members to change their behavior based on the code of conduct,
An information processing device including a control unit that performs control. The control unit
Based on the acquired sensor data, the issues faced by the specific community are estimated, and
The information processing device according to claim 1, wherein the code of conduct for solving the problem is automatically generated. The information processing device according to claim 1, wherein the control unit indirectly urges the member to change the behavior so that the member changes the behavior. The control unit
Set the objective variable as the code of conduct,
A relationship graph showing the relationship between factor variables starting from the objective variable is generated.
The information processing apparatus according to claim 3, wherein among the factor variables related to the objective variable, the information processing apparatus according to claim 3 prompts the member to change the behavior of the factor variable to be intervened, which can change the behavior. The control unit
The information processing apparatus according to claim 4, wherein the information processing apparatus works on the member so that the factor variable related to the objective variable approaches a desired value. The control unit
A causal graph is generated by estimating the factor variable that is presumed to be the cause of the objective variable set as the code of conduct.
The information processing apparatus according to claim 4, wherein the member is encouraged so that the factor variable presumed to be the cause of the objective variable approaches a desired value. The control unit
Based on the acquired sensor data, the standard values in the specific community are automatically generated as a code of conduct.
The information processing device according to claim 3, wherein the member is indirectly encouraged to change his / her behavior based on the standard value. The control unit
The information processing device according to claim 7, wherein the average value of the values of a plurality of members belonging to the specific community is used as a reference value. The control unit
The information processing apparatus according to claim 7, wherein the values of a specific member among the plurality of members belonging to the specific community are set as reference values. The control unit
The information processing device according to claim 7, wherein the information processing apparatus according to claim 7 indirectly promotes behavior change by presenting a reference value to a specific member who is separated from the reference value by a certain amount or more. The control unit
Based on the acquired sensor data, the issues faced by the members belonging to the specific community are estimated.
The information processing device according to claim 2, wherein a code of conduct related to the life rhythm of a member belonging to the specific community is automatically generated in order to solve the problem. The control unit
The information processing device according to claim 11, which automatically generates a code of conduct that synchronizes the living rhythms of a plurality of members belonging to the specific community in order to solve the problem. The control unit
The information processing device according to claim 12, wherein a specific member who is separated from another member belonging to the specific community for a specific life rhythm is indirectly encouraged to change his / her behavior. The control unit
The information processing device according to claim 11, wherein a code of conduct for making the living rhythms of a plurality of members belonging to the specific community asynchronous in order to solve the problem is automatically generated. The control unit
In the first living behavior, when it is detected that a certain number or more of the plurality of members belonging to the specific community are synchronized, the second living behavior that is expected to come after the first living behavior is performed. The information processing device according to claim 14, wherein the behavioral change is indirectly promoted to a specific member belonging to the specific community. According to the code of conduct in the specific community that was automatically generated in advance based on the sensor data obtained by sensing the members belonging to the specific community.
According to the sensor data obtained by sensing the member belonging to the specific community.
Encourage the members to change their behavior,
An information processing device including a control unit that performs control. The processor
Acquiring sensor data obtained by sensing members belonging to a specific community,
To automatically generate a code of conduct in the specific community based on the acquired sensor data,
Encouraging the members to change their behavior based on the code of conduct,
Information processing methods, including. Computer,
Acquire sensor data obtained by sensing members belonging to a specific community,
Based on the acquired sensor data, the code of conduct in the specific community is automatically generated.
Encourage the members to change their behavior based on the code of conduct,
A recording medium in which a program for functioning as a control unit for controlling is recorded.

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